---
title: "Chip Shortage: Google Limits Meta's Access to Gemini Models Due to Lack of Capacity"
description: "Google is forced to limit Meta's access to Gemini models due to a critical shortage of computing power. This has forced the social network to accelerate its transition to its own technologies and invest billions in building data centers. 🚀📉"
date: 2026-06-28T23:49:44.000Z
lang: en
url: https://xab.info/en/posts/chip-shortage-google-limits-metas-access-to-gemini-models-due-to-lack-of-capacity
tags: []
publisher: "XAB.info"
---

# Chip Shortage: Google Limits Meta's Access to Gemini Models Due to Lack of Capacity

![Smartphone displaying the Google app interface, with a blurred Google logo in the background — illustrating the news that Google has restricted Meta’s access to Gemini models due to chip shortages](https://xab.info/media/2026/06/29/google-ogranichil-dostup-meta-k-gemini-defitsit-moshchnostei/google-ogranichil-dostup-meta-k-gemini-defitsit-moshchnostei-1.webp)

A major infrastructure crisis is unfolding in the technology sector. Google, part of the Alphabet conglomerate, is forced to impose strict quotas on the use of its Gemini artificial intelligence models for key corporate clients. These measures have most notably affected Meta Platforms, whose requests for cloud computing have exceeded the supplier's available limits.

According to an investigation by the Financial Times, a shortage of available capacity for inference (running ready-made AI models) was detected as early as March. Google officially notified Meta's management of its inability to fully cover the required volumes of computing capacity. Although a number of other major clients were subject to restrictions, it was Meta that faced critical delays, triggering the destabilization of schedules for several internal projects.

### Crisis in Moderation Architecture

Before the restrictions were introduced, Meta actively integrated commercial Gemini APIs into its services. Third-party algorithms were used for resource-intensive tasks: moderating user content, detecting fraud schemes, automating support, and writing code. Despite the development of its own Llama lineup, Meta engineers preferred Gemini due to its high efficiency in specific scenarios.

In conditions of shortage, Meta's management ordered divisions to switch to a mode of strict token economy and prompt engineering optimization. This became a forced measure to maintain the operability of services amid the reduction of available "fuel" for neural networks.

### Fleeing to Sovereign Infrastructure

The situation has accelerated Meta's strategic turn towards independence. To reduce dependence on external suppliers, the company has activated the deployment of its own Muse Spark model, developed by the Superintelligence Labs division. The goal is to completely replace Gemini's functions within the corporate security loop of social networks.

At the same time, Meta is increasing capital expenditures (CapEx). According to the approved budget, investments in AI infrastructure and data center construction will range from $115 to $135 billion. By 2028, the company plans to allocate up to $600 billion to expand its own capacity within the US, aiming to create a fully autonomous ecosystem.

### Physical Limits of Growth

The precedent of access rationing demonstrates a fundamental problem in the industry: the physical pace of scaling data centers and purchasing semiconductor chips cannot keep up with exponential demand. Alphabet CEO Sundar Pichai previously acknowledged the existence of short-term limitations, noting that the cloud division's revenue could grow faster if equipment were available.

To mitigate the shortage, Google signed an agreement with SpaceX in June to rent computing power. The deal is valued at $920 million per month and involves about 110,000 graphics processing units (GPUs). This solution is intended to serve as an interim buffer until new data centers are built.

Limiting API usage limits is standard practice for cloud providers during periods of peak load. However, the scale of restrictions imposed on one of the largest market players signals that the AI arms race has hit hard physical barriers.